Heteroskedasticity as a leading indicator of desertification in spatially explicit data

Regime shifts are abrupt transitions between alternate ecosystem states including desertification in arid regions due to drought or overgrazing. Regime shifts may be preceded by statistical anomalies such as increased autocorrelation, indicating declining resilience and warning of an impending shift...

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Detalles Bibliográficos
Autores: Seekell, David A., Dakos, Vasilis
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2015
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/116756
Acceso en línea:http://hdl.handle.net/10261/116756
Access Level:acceso abierto
Palabra clave:Critical transitions
Early warning indicators
Heteroskedasticity
Regime shift
Resiliense
Spatial autocorrelation
Spatial pattern
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spelling Heteroskedasticity as a leading indicator of desertification in spatially explicit dataSeekell, David A.Dakos, VasilisCritical transitionsEarly warning indicatorsHeteroskedasticityRegime shiftResilienseSpatial autocorrelationSpatial patternRegime shifts are abrupt transitions between alternate ecosystem states including desertification in arid regions due to drought or overgrazing. Regime shifts may be preceded by statistical anomalies such as increased autocorrelation, indicating declining resilience and warning of an impending shift. Tests for conditional heteroskedasticity, a type of clustered variance, have proven powerful leading indicators for regime shifts in time series data, but an analogous indicator for spatial data has not been evaluated. A spatial analog for conditional heteroskedasticity might be especially useful in arid environments where spatial interactions are critical in structuring ecosystem pattern and process. We tested the efficacy of a test for spatial heteroskedasticity as a leading indicator of regime shifts with simulated data from spatially extended vegetation models with regular and scale-free patterning. These models simulate shifts from extensive vegetative cover to bare, desert-like conditions. The magnitude of spatial heteroskedasticity increased consistently as the modeled systems approached a regime shift from vegetated to desert state. Relative spatial autocorrelation, spatial heteroskedasticity increased earlier and more consistently. We conclude that tests for spatial heteroskedasticity can contribute to the growing toolbox of early warning indicators for regime shifts analyzed with spatially explicit dataPeer reviewedJohn Wiley & SonsConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]201520152015info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/116756reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Ingléshttp://dx.doi.org/10.1002/ece3.1510Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/1167562026-05-22T06:33:51Z
dc.title.none.fl_str_mv Heteroskedasticity as a leading indicator of desertification in spatially explicit data
title Heteroskedasticity as a leading indicator of desertification in spatially explicit data
spellingShingle Heteroskedasticity as a leading indicator of desertification in spatially explicit data
Seekell, David A.
Critical transitions
Early warning indicators
Heteroskedasticity
Regime shift
Resiliense
Spatial autocorrelation
Spatial pattern
title_short Heteroskedasticity as a leading indicator of desertification in spatially explicit data
title_full Heteroskedasticity as a leading indicator of desertification in spatially explicit data
title_fullStr Heteroskedasticity as a leading indicator of desertification in spatially explicit data
title_full_unstemmed Heteroskedasticity as a leading indicator of desertification in spatially explicit data
title_sort Heteroskedasticity as a leading indicator of desertification in spatially explicit data
dc.creator.none.fl_str_mv Seekell, David A.
Dakos, Vasilis
author Seekell, David A.
author_facet Seekell, David A.
Dakos, Vasilis
author_role author
author2 Dakos, Vasilis
author2_role author
dc.contributor.none.fl_str_mv Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Critical transitions
Early warning indicators
Heteroskedasticity
Regime shift
Resiliense
Spatial autocorrelation
Spatial pattern
topic Critical transitions
Early warning indicators
Heteroskedasticity
Regime shift
Resiliense
Spatial autocorrelation
Spatial pattern
description Regime shifts are abrupt transitions between alternate ecosystem states including desertification in arid regions due to drought or overgrazing. Regime shifts may be preceded by statistical anomalies such as increased autocorrelation, indicating declining resilience and warning of an impending shift. Tests for conditional heteroskedasticity, a type of clustered variance, have proven powerful leading indicators for regime shifts in time series data, but an analogous indicator for spatial data has not been evaluated. A spatial analog for conditional heteroskedasticity might be especially useful in arid environments where spatial interactions are critical in structuring ecosystem pattern and process. We tested the efficacy of a test for spatial heteroskedasticity as a leading indicator of regime shifts with simulated data from spatially extended vegetation models with regular and scale-free patterning. These models simulate shifts from extensive vegetative cover to bare, desert-like conditions. The magnitude of spatial heteroskedasticity increased consistently as the modeled systems approached a regime shift from vegetated to desert state. Relative spatial autocorrelation, spatial heteroskedasticity increased earlier and more consistently. We conclude that tests for spatial heteroskedasticity can contribute to the growing toolbox of early warning indicators for regime shifts analyzed with spatially explicit data
publishDate 2015
dc.date.none.fl_str_mv 2015
2015
2015
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/116756
url http://hdl.handle.net/10261/116756
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv http://dx.doi.org/10.1002/ece3.1510
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv John Wiley & Sons
publisher.none.fl_str_mv John Wiley & Sons
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.81155

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